• Journal of muscle and joint health
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• v.11 no.2
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• pp.165-175
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• 2004

Purpose: To describe the psychosocial adaptation, physical disability and social support, and to examine whether the physical disability and social support influence the psychosocial adaptation of women with Rheumatoid Arthritis(RA). Method: This survey was conducted with 102 women diagnosed as RA using a structured survey tool between April 12th and 30th 2004. Results: The Physical disability ranged from 0 to 51, the average was 9.89(${\pm}12.15$), appearing that less severe than previous studies. The social support ranged from 29 to 168, and the average was 91.73(${\pm}31.44$). The age, marital status, and monthly income were associated with patient's perceived social support. The psychosocial adaptation ranged from 77 to 186 and the average was 132.12(${\pm}24.13$). Entering physical disability and social support into the model significantly improved the prediction of psychosocial adaptation: 45.1% of the variance of psychosocial adaptation was attributed by the physical disability (Beta=-.325) and the social support (Beta=.204). Additionally, the religion (Beta=.231) and monthly income (Beta=.381) were significant predictors of the psychosocial adaptation. Conclusions: (1) Programs to improve physical disability of the clients are needed. (2) Marital status and age should be considered when the programs are developed. (3) More social support should be provided to the women with RA. (4) Adequate financial support is essential for the psychosocial adaptation of women with RA.

• Journal of Preventive Medicine and Public Health
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• v.48 no.1
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• pp.1-9
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• 2015

The purpose of the present review is to give an overview of the association between alcohol intake and the risk of developing cancer. Two large-scale expert reports; the World Cancer Research Fund (WCRF)/American Institute of Cancer Research (AICR) report from 2007, including its continuous update project, and the International Agency for Research of Cancer (IARC) monograph from 2012 have extensively reviewed this association in the last decade. We summarize and compare their findings, as well as relate these to the public health impact, with a particular focus on region-specific drinking patterns and disease tendencies. Our findings show that alcohol intake is strongly linked to the risk of developing cancers of the oral cavity, pharynx, larynx, oesophagus, colorectum (in men), and female breast. The two expert reports diverge on the evidence for an association with liver cancer and colorectal cancer in women, which the IARC grades as convincing, but the WCRF/AICR as probable. Despite these discrepancies, there does, however, not seem to be any doubt, that the Population Attributable Fraction of alcohol in relation to cancer is large. As alcohol intake varies largely worldwide, so does, however, also the Population Attributable Fractions, ranging from 10% in Europe to almost 0% in countries where alcohol use is banned. Given the World Health Organization's prediction, that alcohol intake is increasing, especially in low- and middle-income countries, and steadily high in high-income countries, the need for preventive efforts to curb the number of alcohol-related cancers seems growing, as well as the need for taking a region- and gender-specific approach in both future campaigns as well as future research. The review acknowledges the potential beneficial effects of small doses of alcohol in relation to ischaemic heart disease, but a discussion of this lies without the scope of the present study.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.171-178
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• 2016

In this study, a prediction model for depression among parents with disabled children was developed by verifying the effects of social support and family resilience. One hundred forty one parents with disabled children were recruited from three out-patient clinics of rehabilitation hospitals in D city between August and September, 2014. The instruments used were the QRS, CES-D, MSPSS, and KFSS-II. The average score of depression was 20.18/60. The levels of depression were significantly different among variables, e.g., sex, age, and monthly income. The mean scores of the item for social support and family resilience were 3.11/5 and 3.32/5, respectively. Family resilience differed significantly according to monthly income. Parental depression was negatively correlated with the social supports and family resilience. Social support was correlated positively with family resilience. In conclusion, family resilience and social support are predictable factors for depressed parents with disabled children.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.207-224
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• 2018

In this article, an empirical study was conducted by using public dataset from Lending Club Corporation, the largest online peer-to-peer (P2P) lending in the world. We explore significant predictor variables related to P2P lending default that housing situation, length of employment, average current balance, debt-to-income ratio, loan amount, loan purpose, interest rate, public records, number of finance trades, total credit/credit limit, number of delinquent accounts, number of mortgage accounts, and number of bank card accounts are significant factors to loan funded successful on Lending Club platform. We developed online P2P lending default prediction models using discriminant analysis, logistic regression, neural networks, and decision trees (i.e., CART and C5.0) in order to predict P2P loan default. To verify the feasibility and effectiveness of P2P lending default prediction models, borrower loan data and credit data used in this study. Empirical results indicated that neural networks outperforms other classifiers such as discriminant analysis, logistic regression, CART, and C5.0. Neural networks always outperforms other classifiers in P2P loan default prediction.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.45-55
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• 2022

The size of the cryptocurrency market is growing. For example, market capitalization of bitcoin exceeded 500 trillion won. Accordingly, many studies have been conducted to predict the price of cryptocurrency, and most of them have similar methodology of predicting stock prices. However, unlike stock price predictions, machine learning become best model in cryptocurrency price predictions, conceptually cryptocurrency has no passive income from ownership, and statistically, cryptocurrency has at least three times higher liquidity than stocks. Thats why we argue that a methodology different from stock price prediction should be applied to cryptocurrency price prediction studies. We propose Reverse Walk-forward Validation (RWFV), which modifies Walk-forward Validation (WFV). Unlike WFV, RWFV measures accuracy for Validation by pinning the Validation dataset directly in front of the Test dataset in time series, and gradually increasing the size of the Training dataset in front of it in time series. Train data were cut according to the size of the Train dataset with the highest accuracy among all measured Validation accuracy, and then combined with Validation data to measure the accuracy of the Test data. Logistic regression analysis and Support Vector Machine (SVM) were used as the analysis model, and various algorithms and parameters such as L1, L2, rbf, and poly were applied for the reliability of our proposed RWFV. As a result, it was confirmed that all analysis models showed improved accuracy compared to existing studies, and on average, the accuracy increased by 1.23%p. This is a significant improvement in accuracy, given that most of the accuracy of cryptocurrency price prediction remains between 50% and 60% through previous studies.

• The Journal of Information Systems
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• v.29 no.2
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• pp.287-306
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• 2020

Purpose Youth unemployment is a social problem that continues to emerge in Korea. In this study, we create a model that predicts the employment of college graduates using decision tree, random forest and artificial neural network among machine learning techniques and compare the performance between each model through prediction results. Design/methodology/approach In this study, the data processing was performed, including the acquisition of the college graduates' vocational path survey data first, then the selection of independent variables and setting up dependent variables. We use R to create decision tree, random forest, and artificial neural network models and predicted whether college graduates were employed through each model. And at the end, the performance of each model was compared and evaluated. Findings The results showed that the random forest model had the highest performance, and the artificial neural network model had a narrow difference in performance than the decision tree model. In the decision-making tree model, key nodes were selected as to whether they receive economic support from their families, major affiliates, the route of obtaining information for jobs at universities, the importance of working income when choosing jobs and the location of graduation universities. Identifying the importance of variables in the random forest model, whether they receive economic support from their families as important variables, majors, the route to obtaining job information, the degree of irritating feelings for a month, and the location of the graduating university were selected.

• Culinary science and hospitality research
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• v.6 no.2
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• pp.135-155
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• 2000

The main purpose of this study is to investigate the factors affecting customer satisfactions toward the italic restaurants of hotels. Especially, the role of menu-related factors is elaborated. Based on the previous research findings, the following hypotheses were proposed and tested. First, customer evaluations of the factors related to the service of italic hotel restaurants wi11 show differences, depending upon demographics. The results found are as follows. Concerning the seasonality and variety of menu, customer evaluations differed by gender. Depending on age groups, customer evaluations differed for the communicative quality of menu, the restaurant atmosphere, the employee service level, and the food taste. By the type of occupations, there were differences in customer evaluations of the communicative quality of menu, the employee service level, and tie food taste. By the education levels, there were differences in the evaluations toward the seasonality and variety of menu, the restaurant atmosphere, the employee service level, and the food taste, Finally. concerning the restaurant atmosphere and the food taste, customer evaluations differed by their income levels. Second, the employee service level, the seasonality and variety of menu, the communicative quality of menu, the restaurant atmosphere, and the food taste are predicted to significantly affect customer satisfactions, My results were consistent with this prediction except for that the communicative quality of menu did not significantly affect customer satisfactions. Regarding the role of menu-related factors in customer satisfactions, my finding implies the importance of updating the menu, providing the variety and reflecting the seasonality. The more studies, however, should be needed to explore the various roles of menu-related factors in restaurant customer satisfactions.

• Journal of Korean Academy of Nursing Administration
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• v.20 no.5
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• pp.558-566
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• 2014

Purpose: This study was done to measure the level of social support, self-esteem, and empowerment and to identify any effect of social support and self-esteem on the empowerment of nurses. Methods: The study design was a descriptive survey using questionnaires which were given to 381 nurses in C province. The collected data were analyzed using descriptive analysis, t-test, ANOVA, Pearson correlation coefficient, and multiple regressions. Results: The mean score for nurses' empowerment was $2.83{\pm}0.66$. Seven individual characteristics, social support(family, meaningful persons, supervisors, and co-workers) and self-esteem accounted for 23.3% of the variance in nurses' empowerment. Prediction elements influencing empowerment of nurses were salary per month, self-esteem, and social support(supervisors). Conclusion: The results indicate that it is necessary to increase nurses' empowerment. Social support by supervisors and self-esteem were confirmed as important factors to increase nurses' empowerment. In addition, raising the monthly average income would increase empowerment of nurses.

• The Journal of Fisheries Business Administration
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• v.47 no.4
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• pp.65-75
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• 2016

The purpose of this study was to analyze the production and marketing control effects of aqua-cultured flounder required for stable income growth of aqua-cultured household. We analyzed the supply and demand structure of cultured flounder using the partial equilibrium model approach. And we estimated the optimal yield of cultured flounder and analyzed the effect of marketing control through constructed model. The main results of this study are summarized as follows. First, the fitness and predictive power of the estimated model showed that the RMSPE and MAPE values were less than 5% and Theil's inequality coefficient was very close to 0 rather than 1. It was evaluated that the prediction ability of the aqua-cultured flounder supply and demand model by dynamic simulation was excellent. Second, dynamic simulation based on policy simulation was conducted to analyze the price increase effect of production and shipment control of cultured flounder. As a result, if the annual production volume is reduced by 1%, 5%, and 10% among 32,852~37,520 tons, it is analyzed that the price increase effect is from 1.2% to 12.5%. Finally, this study suggests that the production and marketing control can increase the price of aqua-cultured flounder in the market. In this paper, we propose a policy implementation of the total supply system instead of conclusions.

• Korean Journal of Artificial Intelligence
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• v.7 no.2
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• pp.13-17
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• 2019

Currently, the sports market continues to grow every year, and among them, professional baseball's entry income is larger than the rest of the professional league. In sports, strategies are used differently in different situations, and the analysis is based on data to decide which direction to implement. There is a part that a person misses in an analysis, and there is a possibility of a false analysis by subjective judgment. So, if this data analysis is done through artificial intelligence, the objective analysis is possible, and the strategy can be more rationalized, which helps to win the game. The most popular baseball to be applied to artificial intelligence to analyze athletes' strengths and weaknesses and then efficiently establish strategies to ease the competition. The data applied to the experiment were provided on the KBO official website, and the algorithms for forecasting applied linear regression. The results showed that the accuracy was 87%, and the standard error was ±5. Although the results of the experiment were not enough data, it would be possible to effectively use baseball strategies and predict the results of the game if the amount of data and regular data can be applied in the future.